level 2: 10、案例：编写爬虫爬取豆瓣电影排行榜(电影名称，评分)，保存为csv文件     a、用numpy加载csv数据     b、把评分列转换成float64类型     c、计算电影的平均评分     d、求评分最高的电影     e、求评分在9分以上的电影 """ import requests from lxml import etree import csv import numpy as np def getHtml():     url = 'https://movie.douban.com/chart'     headers = {
           "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:67.0) Gecko/20100101 Firefox/67.0",         'Cookie':'ll="118237"; bid=kQ4wCGaUHxM; dbcl2="198098900:5Dr+gGK65ck"; ck=u-be; _pk_id.100001.4cf6=842ffa65a9a6b8b3.1560771548.1.1560771681.1560771548.; _pk_ses.100001.4cf6=*; __yadk_uid=ACadYi5zL218X3UjCuwIiXTk7lThAmup; __utma=30149280.26375845.1560771555.1560771555.1560771555.1; __utmb=30149280.2.10.1560771555; __utmc=30149280; __utmz=30149280.1560771555.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utma=223695111.1679117071.1560771555.1560771555.1560771555.1; __utmb=223695111.0.10.1560771555; __utmc=223695111; __utmz=223695111.1560771555.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); push_noty_num=0; push_doumail_num=0; _vwo_uuid_v2=D0397C64E4418CF03F84A9F99DED3AE28|9c841c774e9ad1066dc8a2ca931d9a9a; __utmt=1; __utmv=30149280.19809'     }     # 1.请求网页源代码     strHtml = requests.get(url,headers=headers).text     # print(strHtml)     """     页面分析之标题：                       页面分析之评分：         8.9         6.6     """     #2. 数据提取     html = etree.HTML(strHtml)     # 获取到电影名称     tittle = html.xpath('//tr[@class="item"]//a/@title')     print(tittle)     # 获取到评分     grade = html.xpath('//span[@class="rating_nums"]/text()')     print(grade)     # 3.处理数据（使用拉链函数，让数据一一对应）     list=[]     res=zip(tittle,grade)     for i in res:         # 将元组数据保存进列表中         list.append(i)     # print(list)     # 4.保存成csv文件     with open('./doubandianying.csv','w',) as f:         csv_f = csv.writer(f)         # 添加第一行         csv_f.writerow(["title","grade"])         # 将数据遍历存储         for row in list:             csv_f.writerow(row) def loadTxt():     filePath = './doubandianying.csv'     res=np.loadtxt(         filePath,         delimiter=',',         dtype=str,         usecols=(0,1),         skiprows=1     )     return res def chage():     filePath = './doubandianying.csv'     res = np.loadtxt(         filePath,         delimiter=',',         dtype=str,         usecols=(1),         skiprows=1     )     res = res.astype(np.float)     return res def mean(gradeFloat):     mean = np.mean(gradeFloat)     return mean def movie(gradeFloat):     index = np.argmax(gradeFloat)     # print(index)     filePath = './doubandianying.csv'     title = np.loadtxt(         filePath,         delimiter=',',         dtype=str,         usecols=(0),         skiprows=1     )     # print(title)     return title[index]     # index = np.argmin(gradeFloat) def movies(gradeFloat):     res = gradeFloat[(gradeFloat>9)]     print(res) if __name__ == '__main__':     # 1.爬取数据     getHtml()     # 2.加载数据     lt = loadTxt()     print(lt)     # 3.将分数列转换成浮点类型     gradeFloat = chage()     print(gradeFloat)     print(type(gradeFloat))     # 4.计算电影的平均分     gradeMean = mean(gradeFloat)     print(gradeMean)     # 5.评分最高的电影     movieFirst = movie(gradeFloat)     print(movieFirst)     # 6. q求评分高于9分以上的电影 （无）     movies = movies(gradeFloat)